Introduction

The summer of 2003 in Europe was a sharp reminder that extreme temperatures remain a considerable danger for developed countries. In metropolitan France, nearly 15,000 deaths were recorded between the 1st and the 20thof August 2003. The most vulnerable populations were the elderly, those suffering from chronic diseases, confined to bed, living alone or in social isolation, and outdoor workers123 . Since 2004, preventive actions targeting different vulnerable populations (the elderly, outdoor workers, sportsmen…) and health professionals have been implemented each summer to reduce the health impact related to heat. These actions are reinforced in case of a heat wave alert.

For the sake of clarity, in the following we have used the term “warning proposal” to refer to a heat wave warning based on forecasted meteorological data, the word “alarm” to refer to a public health alarm based on the observed health indicators, and the world “alert” to refer to the final decision taken by the prefect.

The heat wave warning system covers the 96 metropolitan French departements4 (a “department” in France is an administrative district) and runs from the 1st June to the 31st of August. Each day, forecasted meteorological data are analyzed by the weather services (Météo-France www.meteo.fr) and the French Institute for Public Health Surveillance (InVS). A warning proposal may be issued when both the 3-day averaged minimum and maximum forecasted temperatures have a high probability of exceeding predefined local thresholds. Additional information may be used to modify the proposal in order to allow greater adaptability to local climatic and human situations. This includes the intensity of the heat wave, relative humidity, likelihood of thunderstorms, peaks of air pollution, mass gatherings of people etc., and is assessed qualitatively. Warning proposals are transmitted by the InVS to the Ministry of Health, which in turn transmits the information to the prefects of the concerned departments. Information about the upcoming heat wave is communicated to the media and the general population by Météo-France through a vigilance map. The final decision of actually triggering the alert and therefore implementing prevention measures is the responsibility of the departmental prefects, who report directly to the authority of the Minister of the Interior and are in charge of the local emergency preparedness and response plans. Therefore, a warning proposal must contain enough information to motivate the prefects to issue the alert. In the subsequent days, updates provided to the prefect must clearly state when an alert should be ended.

During an alert, the monitoring of the health situation in the departments impacted by the heat wave can provide useful information for decision-makers. For example, real-time health indicators may identify a larger impact than expected, and therefore help orientate or reinforce preventive actions. They may also help in deciding when to end the alert. Such monitoring has been organised by the InVS since 2004, using daily collection of mortality and morbidity data for each department. With access to a growing number of available indicators through the development of a national syndromic surveillance system called SurSaUD®567 , and after several years of feedback and evaluation of the use of these indicators, we appraised a selection of indicators and methods that could be used to support decision making within the time and efficiency constraints of the existing warning system.

Methods

Definition and selection of the indicators. In the framework of the heat wave prevention plan, we conceived the health indicators as decision-support tools which enable rapid action. The accurate retrospective description of the health impacts of the heat wave is addressed elsewhere 8 .

First, we identified possible health indicators associated with heat exposure, identified by reviewing published (using Medline), grey and unpublished literature (obtained from our partners). Then, we defined several criteria to select the most relevant indicators, using more general assessments of health indicator quality9 ;

- Reactivity, i.e. the lag between exposure to heat and a variation in the indicator trend;

- Delay in obtaining data;

- Existence of a clear and common definition used by all data providers

- Possibility that the observed variations are caused by factors other than heat (confounding factors);

- Comparability of the indicator between different geographic areas and time periods;

- Interpretability: possibility of drawing meaningful conclusions from the indicators, in order to promote health actions.

Information to ensure each criterion was met was documented based on the literature review, on a review of the existing surveillance systems used to measure the possible indicators in France, and on expert judgment.

Statistical analysis. Once the indicators were selected, we chose two statistical methods to analyse them during alerts. These statistical methods needed to be able to identify an unusual excess of cases, computed as the difference between the numbers of observed and expected cases. They had to rely on robust statistical hypotheses, and be easy to perform automatically on a daily basis. Considering the strong limitation due to anteriority of data available, we selected the historical limits method when at least two years of data were available, and a method based on control charts when less than two years of data were available.

The historical limits method is performed by the US Centres for Disease Control and Prevention for their “Morbidity and Mortality Weekly Reports”. This method computes the ratio of the observed value on day d of year n to the mean of the observed values at days d-k, d-k+1,…,d-1, d, d+1,…,d+k of years n-1 to n-q. A statistical alarm is triggered when this ratio exceeds 1+2σ/μ, where μ is the mean and σ is the standard deviation computed from all the observations 10 . For our purposes, the observed values on day d of year n were compared to the mean of the observed values for the same day of the same week, during the previous and following weeks, for each year of the historical data.

The control chart method compares the observation of day d with a threshold calculated from previous data. This threshold was set as the mean of the observed values for the same day during the 3 previous weeks, with the addition of 3 standard deviations of these observations 11 .

For both methods, observations have to follow a Gaussian distribution and be independent. Long-term trends are not taken into account. We did not define a “minimum number of cases” required to perform these methods, although we supposed, based on experience, that it would be difficult to detect an increase when less than 20 cases per day occurred.

Use of the health indicators. During an alert, indicators are analysed daily in each concerned InVS’s regional office using one of the statistical methods described above. A statistical alarm on the health indicators initiates an investigation by local epidemiologists in close relation with data providers (hospitals, practitioners…) before being considered a valid alarm. If the alarm is validated, an analysis of the health situation is then transmitted to the health authorities. It may be used to modify existing preventive actions, or to keep a warning in place after temperatures decrease.

Situations where no statistical alarm is triggered but when an indicators shows a sustained increase in activity (several days above usual values), or when several indicators show increasing trends in the same period are also considered for further investigation.

The whole procedure relies on the local expertise of epidemiologists, especially for morbidity indicators which are highly sensitive to external events that may affect the numbers of those seeking care, such as tourist flow or seasonal departures, depending on the region.

Simulations on how the chosen health indicators would react were performed for heat waves observed in 2006 and 2009. We focused on departments which are heterogeneous in terms of population and data availability: Bouches-du-Rhône, Ardèche, Drôme, Vaucluse, Tarn, Haute-Garonne, Tarn-et-Garonne, Rhône,Paris, Hérault and Indre-et-Loire.

Results

Mortality indicators included total mortality, mortality by age group, by sex and by cause. As each cause of mortality may be monitored for different age groups and sex, the number of potential indicators can become very large. The literature is consistent about a higher risk of mortality in older age groups 1213 , whereas results are less clear for the youngest age groups 1314 . Similarly, a higher risk has been observed in women in some13but not all studies14 . Therefore, we did not distinguish age groups (except over 75 years of age) and sex. Indicators are qualitatively assessed in Table 1.

For total mortality, neoplasms and heat-related mortality, the lag between exposure and the increase in mortality seen in previous studies is usually between 0 and 4 days, with the main peak occurring in less than 48 hours121314151617. On the contrary, a lag of 0-15 days has been observed for cardiovascular18192021 and respiratory mortality192022 , limiting the reactivity of these indicators.

The main limitation regarding total mortality is the delay in obtaining the data, which limits the usefulness of following mortality for decision-making purposes during short heat waves. Existing surveillance systems in France include two mortality sources: the total mortality records from the French National Institute for Statistics and Economic Studies (data from 3 000 cities were collected in 2011, representing 80% of national mortality), and mortality by cause, reported by physicians through a system of e-certification of death (currently representing 5% of the national mortality)567 . Total mortality indicators are available on average 3 to 4 days after deaths, but 7 days are required to obtain validated data. Data on in-hospital deaths can be obtained with a lag of 1-2 days. Mortality by cause is available on the day of the death only for a very small percentage of all deaths.

Overall, data quality was considered very good, except for heat-related mortality, which may have been associated with declaration biases (mainly under-reporting).

Table 1 – Possible mortality indicators

**** Very good, *** Good, ** Poor, * Very poor

Indicator

Reactivity

Delay

Definition

Confounding factors

Comparability

Data quality

Interpretability

Total mortality

***

*

****

***

****

****

****

Total mortality by age group

***

*

****

**

****

****

***

Total mortality by sex

***

*

****

**

****

****

**

Neoplasms

**

**

****

**

*

****

**

Cardiovascular causes

*

**

****

**

*

****

**

Respiratory causes

*

**

****

**

*

****

**

Heat-related causes

***

**

**

****

*

**

****

In-hospital deaths

***

****

****

**

***

****

**

The relevance of morbidity indicators is assessed in Table 2. In the literature, the number of hospital emergency visits has been found to usually moderately increase during heat waves 2324 for all age groups: above 75 years old 7 , above 65 years old25, and even for younger age groups25. Reported causes have been dehydration, hyperthermia and heat stroke 72526, renal diseases 2526 , and cardiovascular and cerebrovascular diseases 2527 , visits linked to alcohol or drugs consumption, and violence28 as well as hyponatremia in elderly people 7. The peak has usually been observed 0 to 1 day after the peak in temperature 293031. An increase in emergency calls to 911 has been documented in the United States32, while an increase in emergency calls to NHS direct has been observed in the UK 33. In 2003,France recorded increases of emergency calls to GPs, hospital emergency visits, activity of emergency ambulance services (SAMU) and fire brigades.

In France, total hospital emergency visits by age group are available in each department with a lag of 1 day. In addition, emergency visits by cause are available through OSCOUR®, an emergency departments (ED) network (about 660 ED were involved in 2011, representing more than 50% of the French hospitals) 567 . However, as the recruitment of the hospitals in this network is based on voluntary-membership, some regions are only partially covered and two (Auvergne and the island of Corsica) are not yet covered at all in the French metropolitan area.

Regarding emergency calls, possible indicators are calls to GP’s emergency associations (SOS-Médecins), the activity of the emergency ambulance services (SAMU) and the activity of the fire brigades (Table 2). The SurSaUD® database gathers OSCOUR® ED data and SOS-Médecins data, including the number of emergency calls, administrative and demographic information (age, sex, zip code of residence etc.), the chief health complaint, and for some associations, the diagnosis performed by the GP during a home visit following an emergency call. Data are available for 59 SOS-Médecins associations out of 62, located in the main cities. SOS-Médecins data from the SurSaUD® database are coded with quite good quality using two different thesauruses. In 2011 for example, percentages of chief complaints and diagnosis coded according to the SOS-Médecins thesauruses were approximately 99% and 63% respectively34 .

Other available indicators are the total number of interventions performed by the SAMU and, in some regions, the total number of interventions delegated by the SAMU to the fire brigades. However, differences in organization and definitions between departments limit the interregional comparability of these indicators.

Finally, based on their reactivity, representativity and data quality, we selected one mortality indicator and four morbidity indicators to be investigated on a systematic basis during a heat wave as follows:

It is of course possible to investigate additional indicators (by age group, by cause), depending on data availability. In-hospital deaths cannot be used to monitor the situations of those who do not use health services, but only act as an indicator of the gravity of the impact of the heat wave, thereby complementing morbidity indicators.

Table 2 – Possible morbidity indicators

**** Very good, *** Good, ** Poor, * Very poor

Indicator

Reactivity

Delay

Definition

Confounding factors

Comparability

Data quality

Interpretability

SAMU and fire brigades

****

****

****

*

*

**

***

Total emergency visits

****

****

****

*

**

***

****

Visits by age group

****

****

****

*

**

***

***

Visits for hyperthermia

****

****

***

****

*

**

***

Visits for dehydration

****

****

***

***

*

**

**

Visits for hyponatremia

****

****

***

****

*

***

***

Visits for cardiovascular and respiratory causes

**

****

***

*

*

***

**

Visits for kidney diseases

****

****

***

*

*

***

**

Visits for drug abuse, violence

****

****

***

*

*

***

**

Hospital admissions following emergency visits

****

****

****

*

*

***

**

GP emergency visits (SOS Médecins)

****

****

***

*

**

***

Illustration of the use of the selected indicators: 2006 and 2009 heat waves. Two main heat waves were observed in 2006 and 2009. Table 3 presents a summary of heat waves characteristics and of the available health indicators for 2006 and 2009 in the impacted departments.

In July 2006, heat wave warning proposals were issued for several departments, lasting up to 31 days in the Bouches-du-Rhône department. In all departments, the warning proposals were transformed into alerts by the prefects.

During that period, several statistical alarms were observed for total hospital emergency visits, hospital emergency visits for people over 75 years-old and mortality. Similar situations were observed in Hérault, Indre-et-Loire,Paris and Rhône: statistical alarms were observed during several consecutive days for the hospital emergency visits for people over 75 years-old. In Haute-Garonne, isolated statistical alarms were observed, but did not occur during or after the hottest periods.

In 2009, a first warning proposal was issued on the 16th August for the Rhône, while a warning proposal with no subsequent alert was communicated for Drôme, Ardèche, Haute-Garonne, Tarn and Tarn-et-Garonne. Warning proposals were transformed into alerts for all these departments plus Vaucluse, issued on the 18th August. The heat wave ended on the 20th and 21st August, depending on the department. In Vaucluse, no major health impact was observed, but four alarms were issued following analysis of health data – three of these when both methods were used and one using the historical limit method only. One alarm occurred on the 21st August during the heat wave, the other three before the heat wave (11th, 12th, 15th August, when night temperatures were already high) (Figure 1). As temperatures were just slightly below thresholds, taking into account the health information could have led to the warning proposal being issued between the 11th and the 15th August.

In Ardèche, Tarn, Tarn-et-Garonne and Drôme, isolated statistical alarms were observed on some days, but were not associated with and did not parallel warm temperatures. In Tarn and Tarn-et-Garonne, statistical alarms were also observed on the 21st and 22nd August, which may be a lagged effect of the heat episode.

The “hospital emergency visits for heat-related causes” indicator was available in Paris only. It increased consistently with temperatures, but the small number of cases (0 to 12 cases per day, compared with 1,200 visits in total) does not allow any statistical analysis. SOS-Médecins data were not available during these heat waves.

Table 3 – Local meteorological indicators of heat waves (3-day average of temperatures) and daily number of cases for health indicators, during the 2006 and 2009 heat waves (mean [min-max])

French department

Year

3-day average of minimal temperatures

3-day average of maximal temperatures

Emergency visits

Emergency visits>75

Deaths

Emergency visits for heat-related causes

Ardèche

2009

16.5 [10.6:19.6]

30.4 [22.5:37.0]

90 [59:116]

9[2:17]

4 [1:11]

Bouches-du-Rhône

2006

20.6 [9.6:24.5]

31.2 [22.5:36]

1050 [775:1346]

101 [75:130]

35 [22:52]

_

Drôme

2009

17.8 [12.2:21.1]

30.5 [23.1:37.4]

137 [89:167]

13[4:23]

6 [1:12]

_

Haute-Garonne

2006

17.5[8.7:22]

28.9 [21.5:36.5]

313 [244:442]

32 [21:48]

5 [0:10]

1 [0:5]

Hérault

2006

19.9[10.3:24]

30.3[23.6:35]

210 [133:274]

16[7:27]

9 [3:17]

_

Indre et Loire

2006

14.9[6:20]

26.6 [19.0:35.2]

197 [154:241]

22[12:41]

5 [1:14]

_

Paris

2006

16.5 [7.9:21.8]

25.8 [17.8:35.2]

1000 [767:1273]

70[42:101]

40 [23:58]

3[0:13]

Rhône

2006

17.4[7.8:23]

28.2 [17.2:37]

270 [94:321]

23[12:41]

13 [5:23]

_

Tarn

2009

16.0[9.5:20.9]

29.9 [22.5:37.5]

87 [39:132]

12[4:23]

1[0:5]

_

Tarn-et-Garonne

2009

16.3 [10.6:20.3]

29.2 [23.2:35.9]

96[63:122]

11[3:24]

_

Vaucluse

2006

18.9[10:22.6]

31.8 [20.7:37.6]

415 [305:523]

45[28:59]

7 [3:15]

_

Fig. 1: Evolution of hospital emergency visits in the Vaucluse department in August 2009, and alarm thresholds calculated using two methods: either historical limits or control charts.

Black indicates days with a statistical alarm on the indicator

Discussion

The heat warning system in France relies on meteorological forecasts and is able to anticipate dangerous heat waves from 24 to 72 hours in advance. It is included in a national management plan capable of setting up timely appropriate actions, thanks to early warnings. Warning thresholds have been set in order to anticipate a major public health impact and to implement pre-defined actions. During less intense heat waves, the media are used to relay appropriate information to the general population. The system is flexible enough to allow extensive discussion between health and meteorological services, and to modify the rules depending on the local situation.

These findings suggest that in France health indicators may not useful to issue a warning proposal. Indeed, since 2004, the decisions taken based on meteorological forecasts have not been modified by the subsequent analyses of real-time health indicators. No marked peaks in the health indicators have been observed during heat wave periods, except for emergency visits for heat-related causes, which is very sensitive to heat, even though representing very small numbers of daily cases. Nevertheless, health indicators would be fundamentally important when assessing the appropriateness of implemented prevention measures on the general population and the need for additional response services. They are also very useful as they reassure health authorities and give them objective data.

As in general, indicators must be interpreted quickly, we preferred to reduce the number to be considered during a heat wave warning and to harmonise the statistical methods used to analyse them between departments. Based on the literature review and data quality assessment, we considered that mortality, hospital emergency visits and emergency calls were indicators to be kept. Mortality indicators are limited by the delay in obtaining the data, while morbidity indicators are limited by data availability and quality. These latter are considered most attractive, as they are more reactive, and still allow life-saving interventions. However, it is possible that morbidity data could remain stable during a heat wave while mortality increases. For longer heat waves, mortality data can still be used to orientate the actions. Finally, when several heat waves are observed over the same summer in the same department, mortality data can be used to assess the vulnerability of the population and to qualitatively predict how they may react to a new episode. In an initial analysis, we decided to focus on all ages and on the older age group (>75 years old). Younger age groups, especially infants, were not selected as the small numbers render interpretation difficult. There is a need for methods to detect and interpret an increase in time-series with low number of daily cases.

In addition to those indicators routinely used to support decision-making during heat waves, it is important to allow greater adaptability to local climatic and human situations. This is done through qualitatively investigating signals from several indicators that are not statistically analysed, such as emergency visits for infants or for cardiovascular diseases. Close connections with health professionals are also required to collect information which can be investigated later, for instance, asking about a possible connection with heat when physicians report an excess of specific pathologies.

The analysis of real-time indicators highlighted here is no substitute for future epidemiological investigation of the impact of temperature on morbidity. Such indicators are part of the decision-making puzzle and cannot be interpreted in a different context. The challenge therefore is to ensure clear communication of the meaning and limitations of these indicators, and to avoid misinterpretation by health professionals and decision-makers.

Bulletins have been developed by InVS regional offices and are used to communicate at the regional level to the stakeholders. Several models have been developed, which usually include a qualitative assessment of the evolution of the indicators (increase, decrease, stable) 363535363738 .

A feedback on the health surveillance is also produced at the end of the summer 38 . At the national level, a summary of the main points of the regional bulletins transmitted daily to all the stakeholders of the systems during a warning.

These health indicators were chosen specifically for the follow-up of heat waves, in association with an analysis of meteorological forecasts, within a well-defined heat wave prevention plan. The method to select relevant indicators could be applied in other countries planning to use of health indicators within a heat prevention plan.

Health indicators could also been defined to follow-up other extreme weather events (storms, floods, forest fires….). Unlike heat waves, these events are characterized by a great variability of possible scenarios and means of exposure to health risks. Their health impacts are therefore more wide-ranging than the immediate fatalities and physical traumatisms. Epidemiological surveillance must therefore be flexible and reactive in order to adapt to event-specific scenarios and to implemented actions 39 . However, the criteria used to assess the quality of the indicators could also be applied.

Background

In France, past heat waves have been characterized by an excess mortality and morbidity among elderly people, workers, patients suffering from chronic diseases and infants [1] [2] . The warmest heat wave was experienced in 2003, when the excess mortality was estimated to be around 14 800 between the 1 st and 20 th August 2003 (+60%) [1] . This event showed no evidence of short-term mortality displacement [1][3] . To prevent similar events, a national prevention plan was developed in 2004 by the French Ministry of Health.

The impact of this national heat prevention plan on the reduction of the risks, and on the excess mortality and morbidity during heat waves is still to be determined. A key limitation to this evaluation is the lack of events since 2004. The main one occurred in July 2006. Minimum and maximum temperatures were below those observed during the August 2003 heat wave, but July 2006 was the warmest month of July in France since 1950. Using a nation-wide model, it was estimated that if the conditions have been those prevailing before 2003, 6 452 excess deaths should have been recorded during the July 2006 heat wave, while about 2 100 excess deaths were observed [4] . This discrepancy may be interpreted as a decrease in the population’s vulnerability to heat, together with increased awareness of the risks related to extreme temperatures, preventive measures and the set-up of the heat prevention plan.

However, this nation-wide model does not allow taking into account spatial heterogeneity, while in 2003 the impact was found to be extremely variable between cities, with the highest burden paid in Dijon, Le Mans, Lyon and especially Paris (+142% excess mortality for summer 2003) [5] . The heterogeneity is likely to be larger in 2006 than in 2003, due to the geographic spread of the heat wave, and to the fact that the prevention plan was implemented differently in the different cities. An analysis of the impact of the 2006 heat wave at the city level is thus required to gain a better understanding of the mortality response during a heat wave, and of the role of the prevention plan. A key question is to understand if the sustained heat during several days generated an additional burden to the one caused by day-by-day temperatures. Indeed, an analysis of the 2003 heat wave showed that the mortality response during the heat wave was exceptional compared to the temperature-mortality relationship usually observed in France. Models based on temperature only did not fit correctly the mortality peak during the 2003 heat wave. An additional term was then introduced in these models to fit the specific mortality response during the heat wave [6] . It is worth investigating if such effect was still observed in 2006, when the prevention plan was available.

In this paper, we assessed the mortality impact of temperature and ozone during summer 2006 in nine French cities, investigating for an additional effect, and for the influence of the warnings and the implementation of the heat prevention plan.

Material and methods

Study Area

This study was set in nine French cities; Bordeaux, Le Havre, Lille, Lyon, Marseille, Paris, Rouen, Strasbourg and Toulouse

Mortality Data

For each city, all causes daily mortality data (International Classification of Diseases, 10thRevision codes A00-R99), were obtained from the National Institute of Statistics and Economic Studies (Insee) for the period 2000-2006.

Temperature and air pollution data

For the same period, daily minimum and maximum temperatures were obtained from the national meteorological office, Météo-France. Ozone concentrations (8 hours maximum values) were obtained from the local air monitoring networks.

Information on the implementation of the heat prevention plan

During summer, heat wave periods are anticipated using temperature forecasts from the meteorological services Météo-France. An alert is issued when two temperature indicators (minimum temperature averaged over 3 days and maximum temperature averaged over 3 days) have a high probability of being above minimum and maximum thresholds. These thresholds vary geographically; a meteorological station, usually located in the main city, has been chosen per department, and is used to give a warning for the whole department [7] . This warning can lead to the implementation of preventive actions during some days, labelled as heat-action days. Preventive actions during heat-action days include social services calling or visiting vulnerable people, increase staff in hospitals and nursing homes, or dissemination of advices through radio and television spots. Yet, apart from the legally binding actions, no data are available about actions that are effectively implemented at the local level by the different stakeholders (authorities, health professionals, NGOs, employers…). Therefore, information on the dates of warnings and on the implementation of a heat-action days was collected for each city to create a binary variable (0 no action, 1 actions), without further detailed on the implemented actions.

Statistical analysis

We applied a method previously used for the analysis of the 2003 impacts in the same cities, detailed elsewhere [6] . We used a generalised additive model (GAM) with a Poisson distribution that allows for over-dispersion to model the variation of the daily mortality data. We controlled for possible confounders, including long-term trend, season, days of the week, bank holidays, minimum temperature on the current day, maximum temperature on the previous day, and O 3 – 8 hour mean of the current and the previous days (0-1 day lag), following the APHEA-2 (Air Pollution and Health: a European Approach) methodology [8] . The degree of smoothing of the spline function for season and long-term trend was chosen to minimize autocorrelation in the residuals. The temperatures were modelled using 3 degrees of freedom. To explore a possible additional effect, we added a penalized cubic regression spline of time which covers a period of 47 days (27/06-11/08). This period was chosen to well capture the increase of mortality during the heat wave and to allow the analysis of a potential short term mortality displacement. The degree of smoothing of this spline function was again chosen to minimize autocorrelation in the residuals. The daily relative risk of the specific effect of the heat wave was computed as the number of expected deaths estimated by the heat wave spline divided by the number of expected deaths estimated in the absence of a heat wave. The number of deaths in the absence of the heat wave was predicted using references values for temperature and ozone, computed as the mean of the values observed between 2000 and 2005, excluding 2003. The overall number of excess deaths was the difference between the number of expected deaths estimated by the heat wave and the number of expected deaths in the absence of the heat wave.

We also investigated the potential effect of heat action plan on mortality by adding the heat-action days as a 0/1 variable in the models. A meta-analysis was conducted to evaluate the impact of this variable.

Results

The nine cities have a total population of about 11 millions, ranging from 6 million in Paris to 254 585 in Le Havre. The mean daily mortality varied from 5.5 in Le Havre to 94.4 in Paris. The proportion of deaths occurring among people over 65 years ranged from 73% in Lille to 81% in Marseille. Mean minimum temperature varied from 6.6°C in Strasbourg to 10.8°C in Marseille, and mean maximum temperature from 13.9°C in Le Havre to 20.1°C in Marseille. Demographic and climatic characteristics of the cities are presented in Table 1.

The 2006 heat wave was less intense than the 2003-heat wave, and had a large geographical heterogeneity. Between the 11 th and the 27 th July 2006, minimum temperatures above 19°C and maximum temperatures above 34°C were observed in all cities (Table 2). Maximum temperature reached 38.3°C in Toulouse, 37.3°C in Bordeaux and 37°C in Marseille. However, these levels were well below the temperature observed in 2003, then reaching 39 to 40.5°C. Figure 1 compares the distribution of the mean temperature anomalies in July and August i.e. the differences between the daily mean temperatures and the usual values, defined as the means of the daily temperatures observed between 2000 and 2006 (excluding August 2003 and July 2006). While during the August 2003 heat waves, temperatures were outliers in all cities, during the July 2006 heat waves, temperatures were exceptional (i.e. higher than the 95 th percentile of the temperature distribution) only in Bordeaux, Le Havre, Lille and Rouen.

In 2006, the highest ozone concentration was observed in Lille, with levels that were well below those observed in 2003 (Table 2).

Fig. 1: Mean Temperature anomalies in July and August per cities between 2000 and 2006 (in July, dots correspond to the extreme of temperatures during the 2006 heat wave, and in August to extreme of temperatures during the 2003 heat wave)

In the national prevention plan, a heat wave is characterised by a sustained period of minimum and maximum temperature above specific thresholds. Based on observed temperatures, these thresholds were reached in only 4 of the 9 cities investigated. However, because forecasted temperatures may be over-estimated, and because decision-makers can decide to maintain an alert even when the temperatures have fallen below the thresholds, warning periods do not correspond to the strictly observed heat wave periods (Table 3). Heat-action days were decided for more than ten days in most cities. They were consistent with the observed temperatures in Bordeaux, Lyon, Paris and Strasbourg. On the opposite, in Marseille and Toulouse, warnings were issued, but the temperature thresholds were not reached.

The excess relative risk associated to the implementation of heat-action days was non-significant and highly variable between cities. The implementation of heat-action days was associated to a combined loss of relative risk of mortality of -3.3% (IC 95% [-10.3% - +4.4%]) (Table 3).

Table 3 – Heat wave and alert period between the 27/06-11/08 2006 per city, % increase in mortality during the heat-action days

City

Heat wave period(observed temperature > thresholds)

Heat-action days

% increase in mortality during heat-action days

Bordeaux

14 – 21 July

16- 27 July

13.1 [-12.5 : 46.3]

Le Havre

no

no

-

Lille

no

no

-

Lyon

18 – 28 July

01-05 July, 18-29 July

-11.9 [-27.2 : 6.6]

Marseille

no

30 June – 05 July,07 July – 2 August

-8.9 [-29.4 : 17.6]

Paris

19-21 July, 24-27 July

01-05 July, 17-28 July

-4.4 [-13.7 : 5.9]

Rouen

no

no

-

Strasbourg

24-27 July

19-29 July

1.6 [-27.8 : 43.0]

Toulouse

no

16-18 July, 24-28 July

13.8 [-15.3 : 53.1]

For each of the nine cities, Figure 2 presents the variation of the relative risk of mortality of the heat-wave effect between the 27/06/2006 and the 11/08/2006. Compared to the results obtained in 2003, no specific effect of the heat wave was observed in 2006. The maximum daily relative risk varied from 1.45 in Strasbourg ([1.01-2.08]) to 1.04 in Lille [0.92-1.18] (Table 4). In all cities, the variations of the mortality observed in the cities during summer 2006 were explained by the usual daily variations of the ozone and temperature.

Fig. 2: Relative risk associated to the heat wave and 95% confidence interval per city between the 27/06/2006 and the 11/08/2006 (dash lines indicate the days with the maximum RR).

Table 4 – Maximum relative risk per city between the 27/06-11/08 2006, and date of the maximum risk

City

RRmax

IC RRmax(95%)

Date

Bordeaux

1.1

[0.92; 1.33]

2006-07-18

Le Havre

1.28

[0.97; 1.49]

2006-07-19

Lille

1.04

[0.92 ; 1.18]

2006-07-18

Lyon

1.13

[0.97; 1.31]

2006-07-17

Marseille

1.14

[0.98 ; 1.33]

2006-07-19

Paris

1.05

[0.9 ; 1.23]

2006-06-27

Rouen

1.13

[0.78 ; 1.29]

2006-06-27

Strasbourg

1.45

[1.01 ; 2.08]

2006-08-11

Toulouse

1.1

[0.88; 1.39]

2006-07-19

The high temperatures and ozone concentrations resulted in 411 excess deaths between the 27/06 and the 11/08 2006 compared to the 2000-2005 average (2003 excluded) (Table 5). Le Havre and Strasbourg present the highest excess mortality, respectively +15% and +10%. A small harvesting effect was observed mainly in Paris, Lyon and Lille.

Discussion

The 2006 summer was warmer than usual and high temperatures were observed in all cities. However, the criteria for defining a heat wave according to the heat prevention plan were reached only in 4 out of the 9 cities studied. In all cities, we did not observe a specific heat wave effect during the 2006 heat wave, and variations of the mortality were explained by the usual daily variations of the ozone and temperatures.

The high levels of temperature and ozone were responsible for 411 deaths in the nine cities between the 27/06 and the 11/08 2006. Half of the deaths (207) occurred in the four cities were the temperatures exceeds the warning thresholds (Bordeaux, Lyon, Paris and Strasbourg). These results are consistent with the analysis at the national level, describing a lower than expected, but still significant impact of the 2006 heat wave (2 100 excess deaths during the heat wave period) [4] .

This new study also provides further insight into the geographical heterogeneity of the heat wave and of its impacts. In comparison, in 2003, the criteria defining a heat wave were reached in 8 of the 9 same cities. 3 096 extra deaths were recorded in summer 2003, and maximum daily relative risks of mortality during the heat wave ranged from 1.16 in Le Havre to 5.00 in Paris [6] . Le Havre is the only city where the maximum risk of mortality was higher in 2006 than in 2003 (resp 1.28 and 1.16), and where the mortality burden was higher in 2006 (resp 14.5 per 100 000 inhabitants vs – 8.4 per 100 000 inhabitants in July). It is worth underlying that the temperatures were not the warmest in Le Havre compared to other cities, but that they were very unusual compared to the temperatures usually observed in that city in July.

In Bordeaux, Lyon, Paris and Strasbourg were heat waves were identified both in 2003 and 2006, differences in the heat wave intensity or duration might be an explanation of the absence of a heat-wave effect in 2006. The hypothesis of a heat wave effect observed only when intensity and duration exceeds a certain value is consistent with the results obtained in the EuroHeat project, where a larger impact was found for long heat waves, or heat waves lasting several days and characterized by extreme temperatures [10] . Consistently, in the US it was found that most excess risk observed during heat waves was comparable to the independent effects of individual days with the same temperatures. A small specific heat wave effect was observed only when the heat waves lasted more than 4 days [11] . Therefore, we can make the hypothesis that for moderate heat waves, the usual temperature-mortality relationship is observed, while an additional effect is observed during extreme heat waves. We used the terms moderate and extremes in reference to the usual temperatures variations observed in specific cities. Considering the example of Le Havre, the relative intensity of the heat wave seems to be more important than its absolute intensity.

Differences may also be explained by the prevention measures implemented during the 2006 heat waves. In France, the heat prevention plan is tailored to respond to those very exceptional heat waves. Our results confirm that a significant mortality burden was still observed during the 2006 heat wave, even in cities where the observed temperatures remained below the heat warning thresholds. Therefore, efforts to promote short and long-term prevention must be maintained, with an enhance communication of heat-related risks and appropriate behaviours all through summer. We already have indications that the heat prevention plan has changed the awareness of the heat-related risks in the general population. A questionnaire send to 1240 adults aged over 15 showed that 74% of the people had heard, read or seen heat wave prevention materials during the summer. 63% of the people had taken protective measures during the 2006 heat wave, and 73% had taken measure to protect their elderly relatives and friends, including regular visits (39%) and regular phone call (29%) [12] .

Considering all the factors influencing the mortality response during a heat wave, the lower than expected mortality burden observed in 2006 does not allow concluding on the efficiency of the heat prevention plan. However, it is worth underlying the low mortality response observed in those cities where the temperatures exceeded the heat wave thresholds and where heat-action days have been implemented. We found that the implementation of heat-action days was non-significant and highly variable depending on the cities, with a combined excess of relative risk of -3.3% (IC 95% [-10.3%; 4.4%]). The variable heat-action days cover a variety of situations; days when temperatures were really above the thresholds, days when forecasts have been over-estimated and observed temperatures were lower than expected, days when the temperatures were below the thresholds and stakeholders had yet decided to activate the plan. Actions implemented during these days also varied between cities. It is thus not surprising that a large heterogeneity and large confidence intervals are associated with this variable. In addition, as heat action days were activated during most of the heat wave periods in Lyon, Bordeaux and Strasbourg, this variable was highly correlated with the heat wave variable, which limits the capacity to interpret the results. Since 2006, a procedure has been introduced to reduce the impact of forecasting uncertainty on the warning decision, and we have a better knowledge of the action implemented at the local level. Data of better quality should be available to investigate the role of the prevention plan in the analysis of future heat waves.